activity
20222026
most citedREAD: Large-Scale Neural Scene Rendering for Autonomous Driving

5 citations · 5 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2026

DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving

Xiaolu Liu, Yicong Li, Song Wang +3

Recently, world models have been incorporated into the autonomous driving systems to improve the planning reliability. Existing approaches typically predict future states through a…

cs.CV2026

VisionTrim: Unified Vision Token Compression for Training-Free MLLM Acceleration

Hanxun Yu, Wentong Li, Xuan Qu +3

Multimodal large language models (MLLMs) suffer from high computational costs due to excessive visual tokens, particularly in high-resolution and video-based scenarios. Existing to…

cs.CV2025

A Coarse-to-Fine Approach to Multi-Modality 3D Occupancy Grounding

Zhan Shi, Song Wang, Junbo Chen +1

Visual grounding aims to identify objects or regions in a scene based on natural language descriptions, essential for spatially aware perception in autonomous driving. However, exi…

cs.CV2025

MambaMap: Online Vectorized HD Map Construction using State Space Model

Ruizi Yang, Xiaolu Liu, Junbo Chen +1

High-definition (HD) maps are essential for autonomous driving, as they provide precise road information for downstream tasks. Recent advances highlight the potential of temporal m…

cs.CV2025

Uncertainty-Instructed Structure Injection for Generalizable HD Map Construction

Xiaolu Liu, Ruizi Yang, Song Wang +3

Reliable high-definition (HD) map construction is crucial for the driving safety of autonomous vehicles. Although recent studies demonstrate improved performance, their generalizat…

cs.CV2025

Inst3D-LMM: Instance-Aware 3D Scene Understanding with Multi-modal Instruction Tuning

Hanxun Yu, Wentong Li, Song Wang +2

Despite encouraging progress in 3D scene understanding, it remains challenging to develop an effective Large Multi-modal Model (LMM) that is capable of understanding and reasoning…

cs.CV2024

ReliOcc: Towards Reliable Semantic Occupancy Prediction via Uncertainty Learning

Song Wang, Zhongdao Wang, Jiawei Yu +4

Vision-centric semantic occupancy prediction plays a crucial role in autonomous driving, which requires accurate and reliable predictions from low-cost sensors. Although having not…

cs.CV2022★ 5 cited

READ: Large-Scale Neural Scene Rendering for Autonomous Driving

Zhuopeng Li, Lu Li, Zeyu Ma +3

Synthesizing free-view photo-realistic images is an important task in multimedia. With the development of advanced driver assistance systems~(ADAS) and their applications in autono…